Overview

Brought to you by YData

Dataset statistics

Number of variables14
Number of observations372140
Missing cells58950
Missing cells (%)1.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory39.7 MiB
Average record size in memory112.0 B

Variable types

Categorical1
DateTime3
Numeric9
Text1

Alerts

VERSIE has constant value "1.0"Constant
DATUM_BESTAND has constant value "2024-08-23 00:00:00"Constant
PEILDATUM has constant value "2024-08-01 00:00:00"Constant
AANTAL_PAT_PER_DIAG is highly overall correlated with AANTAL_SUBTRAJECT_PER_DIAGHigh correlation
AANTAL_PAT_PER_SPC is highly overall correlated with AANTAL_SUBTRAJECT_PER_SPC and 1 other fieldsHigh correlation
AANTAL_PAT_PER_ZPD is highly overall correlated with AANTAL_SUBTRAJECT_PER_ZPDHigh correlation
AANTAL_SUBTRAJECT_PER_DIAG is highly overall correlated with AANTAL_PAT_PER_DIAGHigh correlation
AANTAL_SUBTRAJECT_PER_SPC is highly overall correlated with AANTAL_PAT_PER_SPCHigh correlation
AANTAL_SUBTRAJECT_PER_ZPD is highly overall correlated with AANTAL_PAT_PER_ZPDHigh correlation
BEHANDELEND_SPECIALISME_CD is highly overall correlated with AANTAL_PAT_PER_SPCHigh correlation
GEMIDDELDE_VERKOOPPRIJS has 58950 (15.8%) missing valuesMissing
AANTAL_SUBTRAJECT_PER_ZPD is highly skewed (γ1 = 21.37810224)Skewed

Reproduction

Analysis started2024-09-07 10:03:47.618745
Analysis finished2024-09-07 10:04:02.010470
Duration14.39 seconds
Software versionydata-profiling v0.0.dev0
Download configurationconfig.json

Variables

VERSIE
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.8 MiB
1.0
372140 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1116420
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 372140
100.0%

Length

2024-09-07T10:04:02.085307image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-07T10:04:02.190398image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
1.0 372140
100.0%

Most occurring characters

ValueCountFrequency (%)
1 372140
33.3%
. 372140
33.3%
0 372140
33.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1116420
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 372140
33.3%
. 372140
33.3%
0 372140
33.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1116420
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 372140
33.3%
. 372140
33.3%
0 372140
33.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1116420
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 372140
33.3%
. 372140
33.3%
0 372140
33.3%

DATUM_BESTAND
Date

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.8 MiB
Minimum2024-08-23 00:00:00
Maximum2024-08-23 00:00:00
2024-09-07T10:04:02.279765image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-07T10:04:02.379658image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

PEILDATUM
Date

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.8 MiB
Minimum2024-08-01 00:00:00
Maximum2024-08-01 00:00:00
2024-09-07T10:04:02.473221image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-07T10:04:02.572902image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

JAAR
Date

Distinct13
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.8 MiB
Minimum2012-01-01 00:00:00
Maximum2024-01-01 00:00:00
2024-09-07T10:04:02.672306image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-07T10:04:02.793222image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)

BEHANDELEND_SPECIALISME_CD
Real number (ℝ)

HIGH CORRELATION 

Distinct28
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean462.22138
Minimum301
Maximum8418
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 MiB
2024-09-07T10:04:02.923421image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum301
5-th percentile302
Q1305
median313
Q3322
95-th percentile361
Maximum8418
Range8117
Interquartile range (IQR)17

Descriptive statistics

Standard deviation1080.3317
Coefficient of variation (CV)2.3372604
Kurtosis50.162278
Mean462.22138
Median Absolute Deviation (MAD)8
Skewness7.2180502
Sum1.7201106 × 108
Variance1167116.6
MonotonicityNot monotonic
2024-09-07T10:04:03.066130image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
305 51950
14.0%
313 48500
13.0%
303 42819
11.5%
330 29253
 
7.9%
316 25120
 
6.8%
308 20240
 
5.4%
306 15643
 
4.2%
324 15236
 
4.1%
301 14821
 
4.0%
304 12171
 
3.3%
Other values (18) 96387
25.9%
ValueCountFrequency (%)
301 14821
 
4.0%
302 8210
 
2.2%
303 42819
11.5%
304 12171
 
3.3%
305 51950
14.0%
306 15643
 
4.2%
307 6581
 
1.8%
308 20240
 
5.4%
310 4068
 
1.1%
313 48500
13.0%
ValueCountFrequency (%)
8418 5091
 
1.4%
8416 1637
 
0.4%
1900 247
 
0.1%
390 1043
 
0.3%
389 3899
 
1.0%
362 4623
 
1.2%
361 2731
 
0.7%
335 3722
 
1.0%
330 29253
7.9%
329 972
 
0.3%
Distinct1906
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size2.8 MiB
2024-09-07T10:04:03.464573image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length3.3526012
Min length2

Characters and Unicode

Total characters1247637
Distinct characters25
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8 ?
Unique (%)< 0.1%

Sample

1st row304
2nd row733
3rd row733
4th row733
5th row733
ValueCountFrequency (%)
101 1601
 
0.4%
402 1533
 
0.4%
301 1505
 
0.4%
403 1500
 
0.4%
201 1432
 
0.4%
203 1389
 
0.4%
401 1250
 
0.3%
404 1244
 
0.3%
409 1216
 
0.3%
302 1200
 
0.3%
Other values (1896) 358270
96.3%
2024-09-07T10:04:04.003364image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 238425
19.1%
0 229798
18.4%
2 165281
13.2%
3 134844
10.8%
5 96291
7.7%
9 89799
 
7.2%
4 88237
 
7.1%
7 73495
 
5.9%
6 65043
 
5.2%
8 53809
 
4.3%
Other values (15) 12615
 
1.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1247637
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 238425
19.1%
0 229798
18.4%
2 165281
13.2%
3 134844
10.8%
5 96291
7.7%
9 89799
 
7.2%
4 88237
 
7.1%
7 73495
 
5.9%
6 65043
 
5.2%
8 53809
 
4.3%
Other values (15) 12615
 
1.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1247637
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 238425
19.1%
0 229798
18.4%
2 165281
13.2%
3 134844
10.8%
5 96291
7.7%
9 89799
 
7.2%
4 88237
 
7.1%
7 73495
 
5.9%
6 65043
 
5.2%
8 53809
 
4.3%
Other values (15) 12615
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1247637
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 238425
19.1%
0 229798
18.4%
2 165281
13.2%
3 134844
10.8%
5 96291
7.7%
9 89799
 
7.2%
4 88237
 
7.1%
7 73495
 
5.9%
6 65043
 
5.2%
8 53809
 
4.3%
Other values (15) 12615
 
1.0%

ZORGPRODUCT_CD
Real number (ℝ)

Distinct6290
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.3989233 × 108
Minimum10501002
Maximum9.9841808 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 MiB
2024-09-07T10:04:04.160100image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum10501002
5-th percentile28999040
Q199799062
median1.4959902 × 108
Q39.9000302 × 108
95-th percentile9.90616 × 108
Maximum9.9841808 × 108
Range9.8791708 × 108
Interquartile range (IQR)8.9020396 × 108

Descriptive statistics

Standard deviation4.2871187 × 108
Coefficient of variation (CV)0.97458366
Kurtosis-1.7329685
Mean4.3989233 × 108
Median Absolute Deviation (MAD)1.1960002 × 108
Skewness0.47212231
Sum1.6370153 × 1014
Variance1.8379387 × 1017
MonotonicityNot monotonic
2024-09-07T10:04:04.458987image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
990004009 2741
 
0.7%
990004007 2686
 
0.7%
990003004 2554
 
0.7%
990004006 2163
 
0.6%
990356076 1998
 
0.5%
131999228 1899
 
0.5%
131999164 1872
 
0.5%
990356073 1838
 
0.5%
131999194 1679
 
0.5%
990003007 1631
 
0.4%
Other values (6280) 351079
94.3%
ValueCountFrequency (%)
10501002 9
< 0.1%
10501003 13
< 0.1%
10501004 13
< 0.1%
10501005 13
< 0.1%
10501007 3
 
< 0.1%
10501008 13
< 0.1%
10501010 13
< 0.1%
10501011 4
 
< 0.1%
11101002 11
< 0.1%
11101003 13
< 0.1%
ValueCountFrequency (%)
998418081 193
0.1%
998418080 175
< 0.1%
998418079 44
 
< 0.1%
998418077 10
 
< 0.1%
998418076 10
 
< 0.1%
998418075 8
 
< 0.1%
998418074 267
0.1%
998418073 265
0.1%
998418072 10
 
< 0.1%
998418071 10
 
< 0.1%

AANTAL_PAT_PER_ZPD
Real number (ℝ)

HIGH CORRELATION 

Distinct10851
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean513.99059
Minimum1
Maximum170328
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 MiB
2024-09-07T10:04:04.607236image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median13
Q3101
95-th percentile1732
Maximum170328
Range170327
Interquartile range (IQR)98

Descriptive statistics

Standard deviation3208.0286
Coefficient of variation (CV)6.2414152
Kurtosis421.92663
Mean513.99059
Median Absolute Deviation (MAD)12
Skewness16.951146
Sum1.9127646 × 108
Variance10291448
MonotonicityNot monotonic
2024-09-07T10:04:04.763562image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 62037
 
16.7%
2 30166
 
8.1%
3 19779
 
5.3%
4 14407
 
3.9%
5 11355
 
3.1%
6 9535
 
2.6%
7 7997
 
2.1%
8 6692
 
1.8%
9 6046
 
1.6%
10 5431
 
1.5%
Other values (10841) 198695
53.4%
ValueCountFrequency (%)
1 62037
16.7%
2 30166
8.1%
3 19779
 
5.3%
4 14407
 
3.9%
5 11355
 
3.1%
6 9535
 
2.6%
7 7997
 
2.1%
8 6692
 
1.8%
9 6046
 
1.6%
10 5431
 
1.5%
ValueCountFrequency (%)
170328 1
< 0.1%
165181 1
< 0.1%
163753 1
< 0.1%
160960 1
< 0.1%
155866 1
< 0.1%
154635 1
< 0.1%
154254 1
< 0.1%
144709 1
< 0.1%
118398 1
< 0.1%
115934 1
< 0.1%

AANTAL_SUBTRAJECT_PER_ZPD
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct11739
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean611.78848
Minimum1
Maximum240002
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 MiB
2024-09-07T10:04:04.919066image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median14
Q3111
95-th percentile1981
Maximum240002
Range240001
Interquartile range (IQR)108

Descriptive statistics

Standard deviation4157.904
Coefficient of variation (CV)6.7963097
Kurtosis721.04331
Mean611.78848
Median Absolute Deviation (MAD)13
Skewness21.378102
Sum2.2767097 × 108
Variance17288166
MonotonicityNot monotonic
2024-09-07T10:04:05.074707image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 59726
 
16.0%
2 29659
 
8.0%
3 19580
 
5.3%
4 14189
 
3.8%
5 11259
 
3.0%
6 9498
 
2.6%
7 7953
 
2.1%
8 6605
 
1.8%
9 5972
 
1.6%
10 5431
 
1.5%
Other values (11729) 202268
54.4%
ValueCountFrequency (%)
1 59726
16.0%
2 29659
8.0%
3 19580
 
5.3%
4 14189
 
3.8%
5 11259
 
3.0%
6 9498
 
2.6%
7 7953
 
2.1%
8 6605
 
1.8%
9 5972
 
1.6%
10 5431
 
1.5%
ValueCountFrequency (%)
240002 1
< 0.1%
232423 1
< 0.1%
231945 1
< 0.1%
230943 1
< 0.1%
227921 1
< 0.1%
227409 1
< 0.1%
226673 1
< 0.1%
223888 1
< 0.1%
218673 1
< 0.1%
216394 1
< 0.1%

AANTAL_PAT_PER_DIAG
Real number (ℝ)

HIGH CORRELATION 

Distinct9805
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7705.723
Minimum1
Maximum242378
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 MiB
2024-09-07T10:04:05.220875image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile38
Q1382
median1678
Q36243
95-th percentile37066
Maximum242378
Range242377
Interquartile range (IQR)5861

Descriptive statistics

Standard deviation18073.504
Coefficient of variation (CV)2.3454651
Kurtosis35.319006
Mean7705.723
Median Absolute Deviation (MAD)1541
Skewness5.1319174
Sum2.8676077 × 109
Variance3.2665155 × 108
MonotonicityNot monotonic
2024-09-07T10:04:05.375394image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9 644
 
0.2%
21 623
 
0.2%
8 591
 
0.2%
25 586
 
0.2%
26 571
 
0.2%
23 570
 
0.2%
2 561
 
0.2%
4 561
 
0.2%
12 561
 
0.2%
5 554
 
0.1%
Other values (9795) 366318
98.4%
ValueCountFrequency (%)
1 473
0.1%
2 561
0.2%
3 542
0.1%
4 561
0.2%
5 554
0.1%
6 554
0.1%
7 552
0.1%
8 591
0.2%
9 644
0.2%
10 473
0.1%
ValueCountFrequency (%)
242378 23
< 0.1%
232901 23
< 0.1%
230961 23
< 0.1%
227993 23
< 0.1%
218540 24
< 0.1%
214503 17
< 0.1%
213510 25
< 0.1%
211573 17
< 0.1%
210409 19
< 0.1%
205333 17
< 0.1%

AANTAL_SUBTRAJECT_PER_DIAG
Real number (ℝ)

HIGH CORRELATION 

Distinct10966
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11273.647
Minimum1
Maximum382003
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 MiB
2024-09-07T10:04:05.525901image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile48
Q1507
median2358
Q39132.75
95-th percentile52858
Maximum382003
Range382002
Interquartile range (IQR)8625.75

Descriptive statistics

Standard deviation27334.111
Coefficient of variation (CV)2.4246023
Kurtosis38.328172
Mean11273.647
Median Absolute Deviation (MAD)2178
Skewness5.3452743
Sum4.1953751 × 109
Variance7.4715363 × 108
MonotonicityNot monotonic
2024-09-07T10:04:05.686538image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
25 499
 
0.1%
4 487
 
0.1%
6 469
 
0.1%
18 467
 
0.1%
3 461
 
0.1%
20 455
 
0.1%
7 455
 
0.1%
19 453
 
0.1%
8 440
 
0.1%
5 438
 
0.1%
Other values (10956) 367516
98.8%
ValueCountFrequency (%)
1 382
0.1%
2 425
0.1%
3 461
0.1%
4 487
0.1%
5 438
0.1%
6 469
0.1%
7 455
0.1%
8 440
0.1%
9 421
0.1%
10 430
0.1%
ValueCountFrequency (%)
382003 23
< 0.1%
370337 23
< 0.1%
370126 23
< 0.1%
357268 23
< 0.1%
348469 25
< 0.1%
344894 24
< 0.1%
341641 19
< 0.1%
323740 20
< 0.1%
315768 17
< 0.1%
310747 17
< 0.1%

AANTAL_PAT_PER_SPC
Real number (ℝ)

HIGH CORRELATION 

Distinct352
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean668166.75
Minimum1610
Maximum1487618
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 MiB
2024-09-07T10:04:05.842643image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1610
5-th percentile39408
Q1249484
median757875
Q31027659
95-th percentile1339748
Maximum1487618
Range1486008
Interquartile range (IQR)778175

Descriptive statistics

Standard deviation422014.47
Coefficient of variation (CV)0.63160053
Kurtosis-1.1885622
Mean668166.75
Median Absolute Deviation (MAD)319328
Skewness-0.027647979
Sum2.4865157 × 1011
Variance1.7809621 × 1011
MonotonicityNot monotonic
2024-09-07T10:04:06.001776image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
880928 5102
 
1.4%
874128 4354
 
1.2%
843977 4347
 
1.2%
894361 4333
 
1.2%
880501 4273
 
1.1%
897741 4212
 
1.1%
765054 4089
 
1.1%
814079 4048
 
1.1%
804332 4031
 
1.1%
1076547 3936
 
1.1%
Other values (342) 329415
88.5%
ValueCountFrequency (%)
1610 130
< 0.1%
1831 138
< 0.1%
1922 131
< 0.1%
2495 173
< 0.1%
2632 193
0.1%
2715 79
 
< 0.1%
2748 60
 
< 0.1%
2890 255
0.1%
4024 301
0.1%
4325 82
 
< 0.1%
ValueCountFrequency (%)
1487618 2975
0.8%
1450393 3048
0.8%
1421775 3564
1.0%
1344612 3543
1.0%
1340888 3441
0.9%
1339748 3413
0.9%
1332667 3545
1.0%
1316694 3463
0.9%
1283011 3576
1.0%
1269426 3354
0.9%

AANTAL_SUBTRAJECT_PER_SPC
Real number (ℝ)

HIGH CORRELATION 

Distinct353
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1094888.6
Minimum1861
Maximum2679230
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 MiB
2024-09-07T10:04:06.162186image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1861
5-th percentile46148
Q1365016
median1111817
Q31804767
95-th percentile2601096
Maximum2679230
Range2677369
Interquartile range (IQR)1439751

Descriptive statistics

Standard deviation765686.55
Coefficient of variation (CV)0.69932827
Kurtosis-0.83138142
Mean1094888.6
Median Absolute Deviation (MAD)701213
Skewness0.34784541
Sum4.0745185 × 1011
Variance5.862759 × 1011
MonotonicityNot monotonic
2024-09-07T10:04:06.324041image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1211827 5102
 
1.4%
1281604 4354
 
1.2%
1216288 4347
 
1.2%
1315699 4333
 
1.2%
1300575 4273
 
1.1%
1342015 4212
 
1.1%
1156077 4089
 
1.1%
1230077 4048
 
1.1%
1207155 4031
 
1.1%
2679230 3936
 
1.1%
Other values (343) 329415
88.5%
ValueCountFrequency (%)
1861 130
< 0.1%
2102 138
< 0.1%
2198 131
< 0.1%
2817 173
< 0.1%
2821 79
 
< 0.1%
2827 60
 
< 0.1%
2894 255
0.1%
3452 193
0.1%
4326 82
 
< 0.1%
4677 301
0.1%
ValueCountFrequency (%)
2679230 3936
1.1%
2677634 3795
1.0%
2672112 3866
1.0%
2626316 3788
1.0%
2601096 3843
1.0%
2555652 3890
1.0%
2493665 3923
1.1%
2486795 3851
1.0%
2183767 3757
1.0%
2066343 3810
1.0%

GEMIDDELDE_VERKOOPPRIJS
Real number (ℝ)

MISSING 

Distinct3776
Distinct (%)1.2%
Missing58950
Missing (%)15.8%
Infinite0
Infinite (%)0.0%
Mean3637.2894
Minimum70
Maximum287220
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 MiB
2024-09-07T10:04:06.621682image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum70
5-th percentile145
Q1480
median1250
Q34210
95-th percentile13905
Maximum287220
Range287150
Interquartile range (IQR)3730

Descriptive statistics

Standard deviation6576.1914
Coefficient of variation (CV)1.8079924
Kurtosis127.99444
Mean3637.2894
Median Absolute Deviation (MAD)1025
Skewness6.7633701
Sum1.1391627 × 109
Variance43246294
MonotonicityNot monotonic
2024-09-07T10:04:06.772974image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
160 2091
 
0.6%
105 2081
 
0.6%
110 1821
 
0.5%
185 1734
 
0.5%
180 1678
 
0.5%
140 1569
 
0.4%
175 1553
 
0.4%
125 1497
 
0.4%
145 1477
 
0.4%
165 1471
 
0.4%
Other values (3766) 296218
79.6%
(Missing) 58950
 
15.8%
ValueCountFrequency (%)
70 226
 
0.1%
75 75
 
< 0.1%
80 362
 
0.1%
85 919
0.2%
90 671
 
0.2%
95 717
 
0.2%
100 1022
0.3%
105 2081
0.6%
110 1821
0.5%
115 1119
0.3%
ValueCountFrequency (%)
287220 8
< 0.1%
148910 3
 
< 0.1%
142835 4
< 0.1%
122155 4
< 0.1%
116765 3
 
< 0.1%
109725 7
< 0.1%
108570 7
< 0.1%
107655 4
< 0.1%
107395 4
< 0.1%
101270 8
< 0.1%

Interactions

2024-09-07T10:03:59.900967image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-07T10:03:50.637524image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-07T10:03:51.883531image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-07T10:03:52.973542image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-07T10:03:54.094650image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-07T10:03:55.202112image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-07T10:03:56.408330image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-07T10:03:57.562121image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-07T10:03:58.682413image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-07T10:04:00.030938image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-07T10:03:50.770354image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-07T10:03:52.011205image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-07T10:03:53.106025image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-07T10:03:54.222497image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-07T10:03:55.332553image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-07T10:03:56.543766image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-07T10:03:57.693793image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-07T10:03:58.810279image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-07T10:04:00.151659image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-07T10:03:50.893346image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-07T10:03:52.127206image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-07T10:03:53.224143image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-07T10:03:54.337722image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-07T10:03:55.579747image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-07T10:03:56.668034image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-07T10:03:57.811673image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-07T10:03:58.926981image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-07T10:04:00.279206image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-07T10:03:51.022983image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-07T10:03:52.251477image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-07T10:03:53.350985image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-07T10:03:54.459592image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-07T10:03:55.701541image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-07T10:03:56.797768image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-07T10:03:57.940368image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-07T10:03:59.051085image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-07T10:04:00.397972image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-07T10:03:51.143858image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-07T10:03:52.369786image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-07T10:03:53.471644image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-07T10:03:54.572094image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-07T10:03:55.813629image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-07T10:03:56.917756image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-07T10:03:58.061779image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-07T10:03:59.165381image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-07T10:04:00.515266image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-07T10:03:51.263698image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-07T10:03:52.486168image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-07T10:03:53.588890image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-07T10:03:54.687213image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-07T10:03:55.925620image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-07T10:03:57.038863image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-07T10:03:58.181508image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-07T10:03:59.279198image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-07T10:04:00.645801image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-07T10:03:51.398597image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-07T10:03:52.616514image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-07T10:03:53.721010image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-07T10:03:54.812783image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-07T10:03:56.049908image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-07T10:03:57.172414image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-07T10:03:58.311828image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-07T10:03:59.539911image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-07T10:04:00.775569image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-07T10:03:51.528717image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-07T10:03:52.740952image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-07T10:03:53.849722image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-07T10:03:54.958110image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-07T10:03:56.173285image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-07T10:03:57.307134image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-07T10:03:58.438452image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-07T10:03:59.663831image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-07T10:04:00.895293image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-07T10:03:51.756226image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-07T10:03:52.857392image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-07T10:03:53.972518image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-07T10:03:55.081392image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-07T10:03:56.289990image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-07T10:03:57.435054image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-07T10:03:58.558460image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-07T10:03:59.779667image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2024-09-07T10:04:06.881015image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
AANTAL_PAT_PER_DIAGAANTAL_PAT_PER_SPCAANTAL_PAT_PER_ZPDAANTAL_SUBTRAJECT_PER_DIAGAANTAL_SUBTRAJECT_PER_SPCAANTAL_SUBTRAJECT_PER_ZPDBEHANDELEND_SPECIALISME_CDGEMIDDELDE_VERKOOPPRIJSZORGPRODUCT_CD
AANTAL_PAT_PER_DIAG1.0000.3480.3280.9880.3300.325-0.0610.037-0.173
AANTAL_PAT_PER_SPC0.3481.0000.0860.3640.9620.089-0.532-0.001-0.358
AANTAL_PAT_PER_ZPD0.3280.0861.0000.3260.0930.9960.008-0.297-0.138
AANTAL_SUBTRAJECT_PER_DIAG0.9880.3640.3261.0000.3630.327-0.0550.046-0.204
AANTAL_SUBTRAJECT_PER_SPC0.3300.9620.0930.3631.0000.100-0.453-0.001-0.386
AANTAL_SUBTRAJECT_PER_ZPD0.3250.0890.9960.3270.1001.0000.013-0.300-0.146
BEHANDELEND_SPECIALISME_CD-0.061-0.5320.008-0.055-0.4530.0131.0000.0470.212
GEMIDDELDE_VERKOOPPRIJS0.037-0.001-0.2970.046-0.001-0.3000.0471.0000.029
ZORGPRODUCT_CD-0.173-0.358-0.138-0.204-0.386-0.1460.2120.0291.000

Missing values

2024-09-07T10:04:01.083471image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-09-07T10:04:01.509199image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

VERSIEDATUM_BESTANDPEILDATUMJAARBEHANDELEND_SPECIALISME_CDTYPERENDE_DIAGNOSE_CDZORGPRODUCT_CDAANTAL_PAT_PER_ZPDAANTAL_SUBTRAJECT_PER_ZPDAANTAL_PAT_PER_DIAGAANTAL_SUBTRAJECT_PER_DIAGAANTAL_PAT_PER_SPCAANTAL_SUBTRAJECT_PER_SPCGEMIDDELDE_VERKOOPPRIJS
01.02024-08-232024-08-012018-01-013133041495990021142847479107997126010961135.0
11.02024-08-232024-08-012018-01-0131373399799008717131863842107997126010962140.0
21.02024-08-232024-08-012018-01-0131373399799019185719713186384210799712601096470.0
31.02024-08-232024-08-012018-01-0131373399000300421213186384210799712601096110.0
41.02024-08-232024-08-012018-01-01313733997990615531863842107997126010968550.0
51.02024-08-232024-08-012018-01-0131373399799009333186384210799712601096375.0
61.02024-08-232024-08-012018-01-0131373399799022113186384210799712601096NaN
71.02024-08-232024-08-012018-01-0131373399799028147717243186384210799712601096180.0
81.02024-08-232024-08-012018-01-0131373399799023444431863842107997126010965670.0
91.02024-08-232024-08-012018-01-0131373399799063113186384210799712601096NaN
VERSIEDATUM_BESTANDPEILDATUMJAARBEHANDELEND_SPECIALISME_CDTYPERENDE_DIAGNOSE_CDZORGPRODUCT_CDAANTAL_PAT_PER_ZPDAANTAL_SUBTRAJECT_PER_ZPDAANTAL_PAT_PER_DIAGAANTAL_SUBTRAJECT_PER_DIAGAANTAL_PAT_PER_SPCAANTAL_SUBTRAJECT_PER_SPCGEMIDDELDE_VERKOOPPRIJS
3721301.02024-08-232024-08-012012-01-013100219919900533809492368856771271070NaN
3721311.02024-08-232024-08-012012-01-01310121207010262940333324639308428856771271070285.0
3721321.02024-08-232024-08-012012-01-01310121207010214524639308428856771271070NaN
3721331.02024-08-232024-08-012012-01-01310121207010241014106324639308428856771271070430.0
3721341.02024-08-232024-08-012012-01-01310121207010221124639308428856771271070NaN
3721351.02024-08-232024-08-012012-01-0131012120701025915207724639308428856771271070290.0
3721361.02024-08-232024-08-012012-01-01310121207010202224639308428856771271070NaN
3721371.02024-08-232024-08-012012-01-013101212070102313714324639308428856771271070890.0
3721381.02024-08-232024-08-012012-01-0131012990003002222463930842885677127107090.0
3721391.02024-08-232024-08-012012-01-0131012120701027212112421624639308428856771271070125.0